Diagnostics · 2023 · 29 citations · 15 references
MedicineComputer ScienceRadiologyArtificial IntelligenceDiagnostic ImagingDiagnostic RadiologyChest RadiographyChest RadiologyChest X-ray InterpretationComputer-aided DiagnosisDeep LearningMedical ImagingArtificial Intelligence-assisted DiagnosisChest Radiography FindingsClinical Utility AnalysisComputer-aided Chest X-ray DiagnosisDeep Learning In RadiologyDiagnostic Accuracy StudiesLarge Language ModelsRadiology Artificial Intelligence
This study evaluates the diagnostic accuracy and clinical utility of two artificial intelligence (AI) techniques: Kakao Brain Artificial Neural Network for Chest X-ray Reading (KARA-CXR), an assistive technology developed using large-scale AI and large language models (LLMs), and ChatGPT, a well-known LLM. The study was conducted to validate the performance of the two technologies in chest X-ray reading and explore their potential applications in the medical imaging diagnosis domain. The study methodology consisted of randomly selecting 2000 chest X-ray images from a single institution's patient database, and two radiologists evaluated the readings provided by KARA-CXR and ChatGPT. The study used five qualitative factors to evaluate the readings generated by each model: accuracy, false findings, location inaccuracies, count inaccuracies, and hallucinations. Statistical analysis showed that KARA-CXR achieved significantly higher diagnostic accuracy compared to ChatGPT. In the 'Acceptable' accuracy category, KARA-CXR was rated at 70.50% and 68.00% by two observers, while ChatGPT achieved 40.50% and 47.00%. Interobserver agreement was moderate for both systems, with KARA at 0.74 and GPT4 at 0.73. For 'False Findings', KARA-CXR scored 68.00% and 68.50%, while ChatGPT scored 37.00% for both observers, with high interobserver agreements of 0.96 for KARA and 0.97 for GPT4. In 'Location Inaccuracy' and 'Hallucinations', KARA-CXR outperformed ChatGPT with significant margins. KARA-CXR demonstrated a non-hallucination rate of 75%, which is significantly higher than ChatGPT's 38%. The interobserver agreement was high for KARA (0.91) and moderate to high for GPT4 (0.85) in the hallucination category. In conclusion, this study demonstrates the potential of AI and large-scale language models in medical imaging and diagnostics. It also shows that in the chest X-ray domain, KARA-CXR has relatively higher accuracy than ChatGPT.
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Studies in Health Technology and Informatics
Yindalon Aphinyanaphongs, Constantin Aliferis · 2007 · 1.3K citations
Large language models propagate race-based medicine
Jesutofunmi A. Omiye, Jenna Lester, Simon Spichak et al. · npj Digital Medicine · 2023 · 344 citations · Full text
Sayantan Biswas, Nicola S. Logan, Leon N. Davies et al. · Ophthalmic and Physiological Optics · 2023 · 88 citations · Full text